BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification
BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification
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DOI：
10.1186/s13321-018-0324-5
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发表时间：
2019-01-05
影响因子：
8.6
通讯作者：
Wishart, David S.
中科院分区：
文献类型：
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作者：
Djoumbou-Feunang, Yannick;Fiamoncini, Jarlei;Wishart, David S.
在过去的20年里，一些用于代谢预测的计算工具已经被开发出来，用于预测经历生物转化或环境降解的小分子的结构。这些工具主要是为了促进吸收、分布、代谢、排泄和毒性（ADMET）研究而开发的，尽管现在越来越多的人对使用这些工具来促进代谢组学和暴露组学研究感兴趣。然而，它们的使用和广泛采用仍然受到几个因素的阻碍，包括它们有限的范围、覆盖范围、可用性和性能。为了解决这些限制，我们开发了BioTransformer，这是一个免费的软件包，用于准确、快速、全面的硅代谢预测和化合物鉴定。BioTransformer将机器学习方法与基于知识的方法相结合，通过其代谢预测工具来预测人体组织（例如肝组织）、人体肠道以及环境（土壤和水中微生物群）中的小分子代谢。对BioTransformer的综合评估表明，它的性能优于两种最先进的商用工具（Meteor Nexus和ADMET Predictor），在相似或相同的约束条件下，在相同的药物、农药、植物化学物质或内源性生物制剂上，其精度和召回率比Meteor Nexus或ADMET Predictor获得的精度和召回率高出7倍。此外，BioTransformer能够100%再现EAWAG途径预测系统预测的转化和代谢物。利用从大鼠实验研究中获得的补充表儿茶素的质谱数据，BioTransformer还能够通过其代谢鉴定工具正确识别先前报道的39种表儿茶素代谢物，并提出28种潜在代谢物，其中17种与9种单同位素质量相匹配，而这些单同位素质量没有发现先前报告的证据。结论biotransformer可以作为一个开放存取的命令行工具或软件库。它可以在https://bitbucket.org/djoumbou/biotransformerjar/上免费获得。此外，它还可以在www.biotransformer.ca上作为开放访问的RESTful应用程序免费获得，它允许用户手动或编程地提交查询，并检索代谢预测或化合物识别数据。
BackgroundA number of computational tools for metabolism prediction have been developed over the last 20years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance.ResultsTo address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found.ConclusionBioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/. Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca, which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data.